{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = {\n",
    "    'model': 'category',\n",
    "    'brand': 'category',\n",
    "    'bodyType': 'category',\n",
    "    'fuelType': 'category',\n",
    "    'gearbox': 'category',\n",
    "    'notRepairedDamage': 'category',\n",
    "    'regionCode': 'category',\n",
    "}\n",
    "\n",
    "df = pd.read_csv('../user_data/df_s.csv', sep=' ', dtype=cat)\n",
    "#df['regionCode_count'] = pd.qcut(df.groupby(['regionCode'])['SaleID'].transform('count'), q=10,labels=range(10))\n",
    "#df['city'] = pd.Categorical(df['regionCode'].apply(lambda x: str(x)[:2]))\n",
    "\n",
    "train_X = df[df.train == 1].drop(['price', 'SaleID', 'regionCode'], axis=1)\n",
    "train_y = df[df.train == 1]['price']\n",
    "train_y_ln = np.log1p(train_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor is finished\n",
      "LGBMRegressor is finished\n",
      "AdaBoostRegressor is finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 5258, in fit\n",
      "    return self._fit(X, y, cat_features, None, None, None, sample_weight, None, None, None, None, baseline,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1981, in _fit\n",
      "    train_params = self._prepare_train_params(\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1867, in _prepare_train_params\n",
      "    train_pool = _build_train_pool(X, y, cat_features, text_features, embedding_features, pairs,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1153, in _build_train_pool\n",
      "    train_pool = Pool(X, y, cat_features=cat_features, text_features=text_features, embedding_features=embedding_features, pairs=pairs, weight=sample_weight, group_id=group_id,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 619, in __init__\n",
      "    self._init(data, label, cat_features, text_features, embedding_features, pairs, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count)\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1135, in _init\n",
      "    self._init_pool(data, label, cat_features, text_features, embedding_features, pairs, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count)\n",
      "  File \"_catboost.pyx\", line 3723, in _catboost._PoolBase._init_pool\n",
      "  File \"_catboost.pyx\", line 3770, in _catboost._PoolBase._init_pool\n",
      "  File \"_catboost.pyx\", line 3611, in _catboost._PoolBase._init_features_order_layout_pool\n",
      "  File \"_catboost.pyx\", line 2602, in _catboost._set_features_order_data_pd_data_frame\n",
      "_catboost.CatBoostError: features data: pandas.DataFrame column 'model' has dtype 'category' but is not in  cat_features list\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/miniconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 5258, in fit\n",
      "    return self._fit(X, y, cat_features, None, None, None, sample_weight, None, None, None, None, baseline,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1981, in _fit\n",
      "    train_params = self._prepare_train_params(\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1867, in _prepare_train_params\n",
      "    train_pool = _build_train_pool(X, y, cat_features, text_features, embedding_features, pairs,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1153, in _build_train_pool\n",
      "    train_pool = Pool(X, y, cat_features=cat_features, text_features=text_features, embedding_features=embedding_features, pairs=pairs, weight=sample_weight, group_id=group_id,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 619, in __init__\n",
      "    self._init(data, label, cat_features, text_features, embedding_features, pairs, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count)\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1135, in _init\n",
      "    self._init_pool(data, label, cat_features, text_features, embedding_features, pairs, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count)\n",
      "  File \"_catboost.pyx\", line 3723, in _catboost._PoolBase._init_pool\n",
      "  File \"_catboost.pyx\", line 3770, in _catboost._PoolBase._init_pool\n",
      "  File \"_catboost.pyx\", line 3611, in _catboost._PoolBase._init_features_order_layout_pool\n",
      "  File \"_catboost.pyx\", line 2602, in _catboost._set_features_order_data_pd_data_frame\n",
      "_catboost.CatBoostError: features data: pandas.DataFrame column 'model' has dtype 'category' but is not in  cat_features list\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/miniconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 5258, in fit\n",
      "    return self._fit(X, y, cat_features, None, None, None, sample_weight, None, None, None, None, baseline,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1981, in _fit\n",
      "    train_params = self._prepare_train_params(\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1867, in _prepare_train_params\n",
      "    train_pool = _build_train_pool(X, y, cat_features, text_features, embedding_features, pairs,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1153, in _build_train_pool\n",
      "    train_pool = Pool(X, y, cat_features=cat_features, text_features=text_features, embedding_features=embedding_features, pairs=pairs, weight=sample_weight, group_id=group_id,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 619, in __init__\n",
      "    self._init(data, label, cat_features, text_features, embedding_features, pairs, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count)\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1135, in _init\n",
      "    self._init_pool(data, label, cat_features, text_features, embedding_features, pairs, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count)\n",
      "  File \"_catboost.pyx\", line 3723, in _catboost._PoolBase._init_pool\n",
      "  File \"_catboost.pyx\", line 3770, in _catboost._PoolBase._init_pool\n",
      "  File \"_catboost.pyx\", line 3611, in _catboost._PoolBase._init_features_order_layout_pool\n",
      "  File \"_catboost.pyx\", line 2602, in _catboost._set_features_order_data_pd_data_frame\n",
      "_catboost.CatBoostError: features data: pandas.DataFrame column 'model' has dtype 'category' but is not in  cat_features list\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/miniconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 5258, in fit\n",
      "    return self._fit(X, y, cat_features, None, None, None, sample_weight, None, None, None, None, baseline,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1981, in _fit\n",
      "    train_params = self._prepare_train_params(\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1867, in _prepare_train_params\n",
      "    train_pool = _build_train_pool(X, y, cat_features, text_features, embedding_features, pairs,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1153, in _build_train_pool\n",
      "    train_pool = Pool(X, y, cat_features=cat_features, text_features=text_features, embedding_features=embedding_features, pairs=pairs, weight=sample_weight, group_id=group_id,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 619, in __init__\n",
      "    self._init(data, label, cat_features, text_features, embedding_features, pairs, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count)\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1135, in _init\n",
      "    self._init_pool(data, label, cat_features, text_features, embedding_features, pairs, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count)\n",
      "  File \"_catboost.pyx\", line 3723, in _catboost._PoolBase._init_pool\n",
      "  File \"_catboost.pyx\", line 3770, in _catboost._PoolBase._init_pool\n",
      "  File \"_catboost.pyx\", line 3611, in _catboost._PoolBase._init_features_order_layout_pool\n",
      "  File \"_catboost.pyx\", line 2602, in _catboost._set_features_order_data_pd_data_frame\n",
      "_catboost.CatBoostError: features data: pandas.DataFrame column 'model' has dtype 'category' but is not in  cat_features list\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/miniconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 5258, in fit\n",
      "    return self._fit(X, y, cat_features, None, None, None, sample_weight, None, None, None, None, baseline,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1981, in _fit\n",
      "    train_params = self._prepare_train_params(\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1867, in _prepare_train_params\n",
      "    train_pool = _build_train_pool(X, y, cat_features, text_features, embedding_features, pairs,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1153, in _build_train_pool\n",
      "    train_pool = Pool(X, y, cat_features=cat_features, text_features=text_features, embedding_features=embedding_features, pairs=pairs, weight=sample_weight, group_id=group_id,\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 619, in __init__\n",
      "    self._init(data, label, cat_features, text_features, embedding_features, pairs, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count)\n",
      "  File \"/opt/miniconda3/lib/python3.9/site-packages/catboost/core.py\", line 1135, in _init\n",
      "    self._init_pool(data, label, cat_features, text_features, embedding_features, pairs, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, feature_names, thread_count)\n",
      "  File \"_catboost.pyx\", line 3723, in _catboost._PoolBase._init_pool\n",
      "  File \"_catboost.pyx\", line 3770, in _catboost._PoolBase._init_pool\n",
      "  File \"_catboost.pyx\", line 3611, in _catboost._PoolBase._init_features_order_layout_pool\n",
      "  File \"_catboost.pyx\", line 2602, in _catboost._set_features_order_data_pd_data_frame\n",
      "_catboost.CatBoostError: features data: pandas.DataFrame column 'model' has dtype 'category' but is not in  cat_features list\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<catboost.core.CatBoostRegressor object at 0x7fa594d80070> is finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DecisionTreeRegressor</th>\n",
       "      <th>LGBMRegressor</th>\n",
       "      <th>AdaBoostRegressor</th>\n",
       "      <th>&lt;catboost.core.CatBoostRegressor object at 0x7fa594d80070&gt;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cv1</th>\n",
       "      <td>871.584992</td>\n",
       "      <td>655.866223</td>\n",
       "      <td>1371.064892</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv2</th>\n",
       "      <td>860.123095</td>\n",
       "      <td>649.241067</td>\n",
       "      <td>1463.266318</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv3</th>\n",
       "      <td>896.849386</td>\n",
       "      <td>669.476983</td>\n",
       "      <td>1447.681405</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv4</th>\n",
       "      <td>879.658865</td>\n",
       "      <td>644.677574</td>\n",
       "      <td>1390.598481</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv5</th>\n",
       "      <td>883.257667</td>\n",
       "      <td>663.420078</td>\n",
       "      <td>1403.291671</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DecisionTreeRegressor  LGBMRegressor  AdaBoostRegressor  \\\n",
       "cv1             871.584992     655.866223        1371.064892   \n",
       "cv2             860.123095     649.241067        1463.266318   \n",
       "cv3             896.849386     669.476983        1447.681405   \n",
       "cv4             879.658865     644.677574        1390.598481   \n",
       "cv5             883.257667     663.420078        1403.291671   \n",
       "\n",
       "     <catboost.core.CatBoostRegressor object at 0x7fa594d80070>  \n",
       "cv1                                                NaN           \n",
       "cv2                                                NaN           \n",
       "cv3                                                NaN           \n",
       "cv4                                                NaN           \n",
       "cv5                                                NaN           "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_absolute_error,  make_scorer\n",
    "from catboost import CatBoostRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from lightgbm.sklearn import LGBMRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "models = [DecisionTreeRegressor(),\n",
    "          LGBMRegressor(),\n",
    "          AdaBoostRegressor(),\n",
    "          CatBoostRegressor(cat_features=[i for i in cat.keys() if i in train_X.columns])]\n",
    "\n",
    "\n",
    "def maee(y_true, y_pred):\n",
    "    loss = mean_absolute_error(np.expm1(y_true), np.expm1(y_pred))\n",
    "    return loss\n",
    "\n",
    "\n",
    "# 五折交叉检验\n",
    "result = dict()\n",
    "for model in models:\n",
    "    model_name = str(model).split('(')[0]\n",
    "    scores = cross_val_score(model, X=train_X, y=train_y_ln,\n",
    "                             verbose=0, cv=5, scoring=make_scorer(maee))\n",
    "    result[model_name] = scores\n",
    "    print(model_name + ' is finished')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "models[3] = CatBoostRegressor(\n",
    "    cat_features=[i for i in cat.keys() if i in train_X.columns])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x7fa58de11100>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.091516\n",
      "0:\tlearn: 1.1205271\ttotal: 143ms\tremaining: 2m 22s\n",
      "1:\tlearn: 1.0344716\ttotal: 354ms\tremaining: 2m 56s\n",
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     ]
    }
   ],
   "source": [
    "model_name = str(model[3]).split('(')[0]\n",
    "scores = cross_val_score(models[3], X=train_X, y=train_y_ln,\n",
    "                         verbose=0, cv=5, scoring=make_scorer(maee))\n",
    "result[model_name] = scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DecisionTreeRegressor</th>\n",
       "      <th>LGBMRegressor</th>\n",
       "      <th>AdaBoostRegressor</th>\n",
       "      <th>&lt;catboost.core.CatBoostRegressor object at 0x7fa594d80070&gt;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cv1</th>\n",
       "      <td>871.584992</td>\n",
       "      <td>655.866223</td>\n",
       "      <td>1371.064892</td>\n",
       "      <td>550.218536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv2</th>\n",
       "      <td>860.123095</td>\n",
       "      <td>649.241067</td>\n",
       "      <td>1463.266318</td>\n",
       "      <td>549.697279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv3</th>\n",
       "      <td>896.849386</td>\n",
       "      <td>669.476983</td>\n",
       "      <td>1447.681405</td>\n",
       "      <td>556.993115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv4</th>\n",
       "      <td>879.658865</td>\n",
       "      <td>644.677574</td>\n",
       "      <td>1390.598481</td>\n",
       "      <td>548.740731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv5</th>\n",
       "      <td>883.257667</td>\n",
       "      <td>663.420078</td>\n",
       "      <td>1403.291671</td>\n",
       "      <td>554.704737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DecisionTreeRegressor  LGBMRegressor  AdaBoostRegressor  \\\n",
       "cv1             871.584992     655.866223        1371.064892   \n",
       "cv2             860.123095     649.241067        1463.266318   \n",
       "cv3             896.849386     669.476983        1447.681405   \n",
       "cv4             879.658865     644.677574        1390.598481   \n",
       "cv5             883.257667     663.420078        1403.291671   \n",
       "\n",
       "     <catboost.core.CatBoostRegressor object at 0x7fa594d80070>  \n",
       "cv1                                         550.218536           \n",
       "cv2                                         549.697279           \n",
       "cv3                                         556.993115           \n",
       "cv4                                         548.740731           \n",
       "cv5                                         554.704737           "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 结果\n",
    "result = pd.DataFrame(result)\n",
    "result.index = ['cv' + str(x) for x in range(1, 6)]\n",
    "result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.iloc[:,3].name='CatBoostRegressor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DecisionTreeRegressor</th>\n",
       "      <th>LGBMRegressor</th>\n",
       "      <th>AdaBoostRegressor</th>\n",
       "      <th>CatBoostRegressor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cv1</th>\n",
       "      <td>871.584992</td>\n",
       "      <td>655.866223</td>\n",
       "      <td>1371.064892</td>\n",
       "      <td>550.218536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv2</th>\n",
       "      <td>860.123095</td>\n",
       "      <td>649.241067</td>\n",
       "      <td>1463.266318</td>\n",
       "      <td>549.697279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv3</th>\n",
       "      <td>896.849386</td>\n",
       "      <td>669.476983</td>\n",
       "      <td>1447.681405</td>\n",
       "      <td>556.993115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv4</th>\n",
       "      <td>879.658865</td>\n",
       "      <td>644.677574</td>\n",
       "      <td>1390.598481</td>\n",
       "      <td>548.740731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv5</th>\n",
       "      <td>883.257667</td>\n",
       "      <td>663.420078</td>\n",
       "      <td>1403.291671</td>\n",
       "      <td>554.704737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DecisionTreeRegressor  LGBMRegressor  AdaBoostRegressor  \\\n",
       "cv1             871.584992     655.866223        1371.064892   \n",
       "cv2             860.123095     649.241067        1463.266318   \n",
       "cv3             896.849386     669.476983        1447.681405   \n",
       "cv4             879.658865     644.677574        1390.598481   \n",
       "cv5             883.257667     663.420078        1403.291671   \n",
       "\n",
       "     CatBoostRegressor  \n",
       "cv1         550.218536  \n",
       "cv2         549.697279  \n",
       "cv3         556.993115  \n",
       "cv4         548.740731  \n",
       "cv5         554.704737  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.iloc[:, [0,1,2,4]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "3d597f4c481aa0f25dceb95d2a0067e73c0966dcbd003d741d821a7208527ecf"
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  "kernelspec": {
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